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Scientific Programming
Volume 2019, Article ID 8043905, 18 pages
https://doi.org/10.1155/2019/8043905
Review Article

Recommendation and Classification Systems: A Systematic Mapping Study

1Computer Languages and Systems Department, University of Seville, Avd. Reina Mercedes s/n, 41012 Seville, Spain
2Statistics and Operational Research Area, University of Malaga, Bulevar Louis Pasteur 31, 29010 Malaga, Spain
3Servinform S.A., Calle Manufactura, 5, 41927 Mairena del Aljarafe, Spain

Correspondence should be addressed to J. G. Enríquez; gro.2twi@zelaznog.esoj

Received 15 February 2019; Revised 14 May 2019; Accepted 12 June 2019; Published 27 June 2019

Guest Editor: Luis Iribarne

Copyright © 2019 J. G. Enríquez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Jaysri, J. Priyadharshini, P. Subathra, and Dr. (Col.) P. N. Kumar, “Analysis and performance of collaborative filtering and classification algorithms,” International Journal of Applied Engineering Research, vol. 10, pp. 24529–24540, 2015. View at Google Scholar
  2. M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, “Collaborative Filtering Recommender Systems,” Foundations and Trends® in Human—Computer Interaction, vol. 4, no. 2, pp. 81–173, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Gunawardana and G. Shani, “A survey of accuracy evaluation metrics of recommendation tasks,” Journal of Machine Learning Research, pp. 2935–2962, 2009. View at Google Scholar
  4. M. Poussevin, V. Guigue, and P. Gallinari, “Extracting a vocabulary of surprise by collaborative filtering mixture and analysis of feelings,” in Proceedings of the CORIA 2015—Conference in Search Infomations and Applications—12th French Information Retrieval Conference, Paris, France, March 2015.
  5. M. Z. Kurdi, “Lexical and syntactic features selection for an adaptive reading recommendation system based on text complexity,” in Proceedings of the 2017 International Conference on Information System and Data Mining, pp. 66–69, Charleston, SC, USA, April 2017.
  6. M. A. Ghazanfar and A. Prügel-Bennett, “An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering,” in Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 (IMECS), Hong Kong, China, 2010.
  7. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, “Methods and metrics for cold-start recommendations,” in Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR ’02, New York, NY, USA, 2002.
  8. M. Ghazanfar and A. Prügel-Bennett, “Building switching hybrid recommender system using machine learning classifiers and collaborative filtering,” IAENG International Journal of Computer Science, vol. 37, no. 3, 2010. View at Google Scholar
  9. Z. Hailong, G. Wenyan, and J. Bo, “Machine learning and lexicon based methods for sentiment classification: a survey,” in Proceedings of the 11th Web Information System and Application Conference (WISA), pp. 262–265, Tianjin, China, September 2014.
  10. R. Mu, “A survey of recommender systems based on deep learning,” IEEE Access, vol. 6, pp. 69009–69022, 2018. View at Google Scholar
  11. I. Portugal, P. Alencar, and D. Cowan, “The use of machine learning algorithms in recommender systems: a systematic review,” Expert Systems with Applications, vol. 97, pp. 205–227, 2018. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Ouhbi, B. Frikh, E. Zemmouri, and A. Abbad, “Deep learning based recommender systems,” IEEE International Colloquium on Information Science and Technology (CiSt), vol. 2018, pp. 161–166, 2018. View at Google Scholar
  13. S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: a survey and new perspectives,” ACM Computing Surveys, vol. 52, no. 1, p. 5, 2019. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” Engineering, vol. 2, p. 1051, 2007. View at Google Scholar
  15. K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, vol. 17, p. 10, Bari, Italy, 2008.
  16. L. Leydesdorff, “Top-down decomposition of the journal citation report of the social science citation index: graph- and factor-analytical approaches,” Scientometrics, vol. 60, no. 2, pp. 159–180, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. J. L. C. Izquierdo, V. Cosentino, and J. Cabot, “Analysis of co-authorship graphs of CORE-ranked software conferences,” Scientometrics, vol. 109, no. 3, pp. 1665–1693, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. SCIE, “La Sociedad Científica Informática de España,” 2017. View at Google Scholar
  19. SCIE, “GII-GRIN-SCIE (GGS) Conference Rating,” 2019.
  20. A. Sattar, M. A. Ghazanfar, and M. Iqbal, “Building accurate and practical recommender system algorithms using machine learning classifier and collaborative filtering,” Arabian Journal for Science and Engineering, vol. 42, no. 8, pp. 3229–3247, 2017. View at Publisher · View at Google Scholar · View at Scopus
  21. T.-D. Nguyen, T.-D. Cao, and L.-G. Nguyen, “DGA botnet detection using collaborative filtering and density-based clustering,” in Proceedings of the Sixth International Symposium on Information and Communication Technology, pp. 203–209, Hue City, Vietnam, December 2015.
  22. T. Xie, Y. Chen, L. Hu, C. Gao, C. Hu, and J. Shen, “A multistage collaborative filtering method for fall detection,” in Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Rio, Brazil, August 2017.
  23. N. Thilagavathi and R. Taarika, “Content based filtering in online social network using inference algorithm,” in Proceedings of the 2014 International Conference on Circuits, Power and Computing Technologies (ICCPCT), Nagercoil, India, March 2014.
  24. X. Su, T. M. Khoshgoftaar, X. Zhu, and R. Greiner, “Imputation-boosted collaborative filtering using machine learning classifiers,” in Proceedings of the 2008 ACM Symposium on Applied Computing—SAC ’08, Fortaleza, Ceará, Brazil, March 2008.
  25. T. Shrot, A. Rosenfeld, J. Golbeck, and S. Kraus, “CRISP -an interruption management algorithm based on collaborative filtering,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, Canada, 2014.
  26. X. Zheng, “A credit scoring model based on collaborative filtering,” in Proceedings of the 9th International Conference on Computational Intelligence and Security, Emei Mountain, Sichuan, China, December 2013.
  27. J. Li, H. Xu, X. He, J. Deng, and X. Sun, “Tweet modeling with LSTM recurrent neural networks for hashtag recommendation,” in Proceedings of the International Joint Conference on Neural Networks, Vancouver, British Columbia, Canada, 2016.
  28. P. Liu, J. Cao, X. Liang, and W. Li, “A two-stage cross-domain recommendation for cold start problem in cyber-physical systems,” in Proceedings of the International Conference on Machine Learning and Cybernetics, Guangzhou, China, 2015.
  29. P. Bedi, Richa, S. K. Agarwal, and V. Bhasin, “ELM based imputation-boosted proactive recommender systems,” in Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, September 2016.
  30. R. H. Nidhi and B. Annappa, “Twitter-user recommender system using tweets: a content-based approach,” in Proceedings of the ICCIDS 2017 International Conference on Computational Intelligence in Data Science, pp. 1–6, Chennai, India, June 2017.
  31. R. Mittal and V. Sinha, “A personalized time-bound activity recommendation system,” in Proceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA, January 2017.
  32. A. S. Vairagade and R. A. Fadnavis, “Automated content based short text classification for filtering undesired posts on Facebook,” in Proceedings of the IEEE World Conference on Futuristic Trends in Research and Innovation for Social Welfare (WCTFTR), Coimbatore, India, 2016.
  33. W. Bhebe and O. P. Kogeda, “Shilling attack detection in collaborative recommender systems using a meta learning strategy,” in Proceedings of the 2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), pp. 56–61, IEEE, Windhoek, Namibia, May 2015.
  34. L. Bhatia and S. S. Prasad, “Building a distributed generic recommender using scalable data mining library,” in Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Communication Technology (CICT), Ghaziabad, India, 2015.
  35. C. Biancalana, F. Gasparetti, A. Micarelli, A. Miola, and G. Sansonetti, “Context-aware movie recommendation based on signal processing and machine learning,” in Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, Chicago, IL, USA, 2011.
  36. T. Zhang and V. S. Iyengar, “Recommender systems using linear classifiers,” Journal of Machine Learning Research, pp. 313–334, 2002. View at Google Scholar
  37. V. Pronk, W. Verhaegh, A. Proidl, and M. Tiemann, “Incorporating user control into recommender systems based on naive Bayesian classification,” in Proceedings of the ACM International Conference on Recommender Systems, Minneapolis, MN, USA, 2007.
  38. R. Burke, B. Mobasher, C. Williams, and R. Bhaumik, “Classification features for attack detection in collaborative recommender systems,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD ’06, Philadelphia, PA, USA, August 2006.
  39. Y. Song, L. Zhang, and C. L. Giles, “Automatic tag recommendation algorithms for social recommender systems,” ACM Transactions on the Web, vol. 5, no. 1, p. 31, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. M. Brovman, “Optimizing similar item recommendations in a semi-structured marketplace to maximize conversion,” in Proceedings of the 10th ACM Conference on Recommender Systems—RecSys ’16, Boston, MA, USA, September 2016.
  41. S. E. Middleton, D. C. De Roure, and N. R. Shadbolt, “Capturing knowledge of user preferences,” in Proceedings of the International Conference on Knowledge capture—K-CAP, Victoria, BC, Canada, 2001.
  42. P. P. Jean-Jacques, J. Noack, and K. Bodarwé, “Emotion-based music recommendation using supervised learning,” in Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia, Linz, Austria, December 2015.
  43. A. Thor and E. Rahm, “AWESOME—A Data Warehouse-Based System for Adaptive Website Recommendations,” in Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 384–395, VLDB Endowment, Toronto, Ontario, Canada, September 2004.
  44. Y. H. Gu, S. J. Yoo, Z. Piao, J. No, Z. Jiang, and H. Yin, “A smart-device news recommendation technology based on the user click behavior,” in Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory, pp. 9–16, Jeju Island, Republic of Korea, October 2016.
  45. X. Li and H. Chen, “Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach,” Decision Support Systems, vol. 54, no. 2, pp. 880–890, 2013. View at Publisher · View at Google Scholar · View at Scopus
  46. A. A. Kothari and W. D. Patel, “A novel approach towards context based recommendations using support vector machine methodology,” Procedia Computer Science, vol. 57, pp. 1171–1178, 2015. View at Google Scholar
  47. W. P. Lee, C. T. Chen, J. Y. Huang, and J. Y. Liang, “A smartphone-based activity-aware system for music streaming recommendation,” Knowledge-Based Systems, vol. 131, pp. 70–82, 2017. View at Publisher · View at Google Scholar · View at Scopus
  48. D. Han, J. Li, W. Li, R. Liu, and H. Chen, An App Usage Recommender System: Improving Prediction Accuracy for Both Warm and Cold Start Users, Multimedia Systems, 2019.
  49. A. Visuri, R. Poguntke, and E. Kuosmanen, Proposing Design Recommendations for an Intelligent Recommender System Logging Stress, Association for Computing Machinery, New York, NY, USA, 2018.
  50. E. R. Núñez-Valdez, D. Quintana, R. G. Crespo, P. Isasi, and E. Herrera-Viedma, “A recommender system based on implicit feedback for selective dissemination of ebooks,” Information Sciences, vol. 467, pp. 87–98, 2018. View at Publisher · View at Google Scholar · View at Scopus
  51. S. Narayan and E. Sathiyamoorthy, “A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases,” Neural Computing and Applications, vol. 31, no. S1, pp. 93–102, 2019. View at Publisher · View at Google Scholar · View at Scopus
  52. A. Pujahari and V. Padmanabhan, “An approach to content based recommender systems using decision list based classification with k-DNF rule set,” in Proceedings of the 2014 13th International Conference on Information Technology (ICIT), Bhubaneswar, India, December 2014.
  53. M. Mehdi, N. Bouguila, and J. Bentahar, “Probabilistic approach for QoS-aware recommender system for trustworthy web service selection,” Applied Intelligence, vol. 41, no. 2, pp. 503–524, 2014. View at Publisher · View at Google Scholar · View at Scopus
  54. R. A. Gotardo, E. R. Hruschka, S. D. Zorzo, and P. R. M. Cereda, “Approach to cold-start problem in recommender systems in the context of web-based education,” in Proceedings of the 2013 12th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, December 2013.
  55. H. Costa, B. Furtado, D. Pires, L. Macedo, and A. Cardoso, “Context and intention-awareness in POIs recommender systems,” in Proceedings of the 6th ACM Recommender Systems Conference, 4th Workshop on Context-Aware Recommender Systems (RecSys), vol. 12, p. 5, Dubai, UAE, September 2012.
  56. U. Rohini and V. Ambati, “A collaborative filtering based re-ranking strategy for search in digital libraries,” in Lecture Notes in Computer Science, Springer, Berlin, Germany, 2005. View at Google Scholar
  57. Y. Z. Wei, L. Moreau, and N. R. Jennings, “Learning users’ interests by quality classification in market-based recommender systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 12, pp. 1678–1688, 2005. View at Publisher · View at Google Scholar · View at Scopus
  58. W. Paireekreng, “Mobile content recommendation system for re-visiting user using content-based filtering and client-side user profile,” in Proceedings—International Conference on Machine Learning and Cybernetics, Lanzhou, China, 2013.
  59. S. Lu, B. Wang, H. Wang, and Q. Hong, “A hybrid collaborative filtering algorithm based on KNN and gradient boosting,” in Proceedings of the 13th International Conference on Computer Science and Education (ICCSE), Colombo, Sri Lanka, August 2018.
  60. L. Zhang, B. Xiao, J. Guo, and C. Zhu, “A scalable collaborative filtering algorithm based on localized preference,” in Proceedings of the 7th International Conference on Machine Learning and Cybernetics (ICMLC), Melbourne, Australia, December 2008.
  61. S. Feng, M. Zhang, Y. Zhang, and Z. Deng, “Recommended or not recommended? Review classification through opinion extraction,” in Proceedings of the 12th Asia-Pacific Web Conference, Advances in Web Technologies and Applications (APWeb), Busan, Korea, April 2010.
  62. B. Alghofaily and C. Ding, “Meta-feature based data mining service selection and recommendation using machine learning models,” in Proceedings of the 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), Xi’an, China, October 2018.
  63. C. Yang, S. Ren, Y. Liu, H. Cao, Q. Yuan, and G. Han, “Personalized channel recommendation deep learning from a switch sequence,” IEEE Access, vol. 6, pp. 50824–50838, 2018. View at Publisher · View at Google Scholar · View at Scopus
  64. M. Tkalčič, A. Odić, A. KoTkalšičir, and J. Tasič, “Affective labeling in a content-based recommender system for images,” IEEE Transactions on Multimedia, vol. 15, no. 2, pp. 391–400, 2013. View at Publisher · View at Google Scholar · View at Scopus
  65. A. A. Kothari and W. D. Patel, “A novel approach towards context sensitive recommendations based on machine learning methodology,” in Proceedings of the 2015 5th International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, MP, India, April 2015.
  66. R. Trepos, A. Salleb, M. O. Cordier, V. Masson, and C. Gascuel, “A distance-based approach for action recommendation,” in Lecture Notes in Computer Science, Springer, Berlin, Germany, 2005. View at Google Scholar
  67. J. S. Pedro and S. Siersdorfer, “Ranking and Classifying Attractiveness of Photos in Folksonomies,” in Proceedings of the 18th International Conference on World Wide Web, pp. 771–780, ACM, Madrid, Spain, April 2009.
  68. T. Raeder, T. R. Hoens, and N. V. Chawla, “Consequences of variability in classifier performance estimates,” in Proceedings of the IEEE International Conference on Data Mining (ICDM), Sydney, Australia, 2010.
  69. J. J. Ahn, S. J. Lee, K. J. Oh, T. Y. Kim, H. Y. Lee, and M. S. Kim, “Machine learning algorithm selection for forecasting behavior of global institutional investors,” in Proceedings of the 42nd Annual Hawaii International Conference on System Sciences (HICSS), Waikoloa, Hawaii, January 2009.
  70. D. Arendt, E. Saldanha, R. Wesslen, S. Volkova, and W. Dou, “Towards rapid interactive machine learning: evaluating tradeoffs of classification without representation,” in Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 591–602, Marina del Ray, CA, USA, March 2019.
  71. A. G. C. de Sá and G. L. Pappa, “Towards a method for automatically evolving bayesian network classifiers,” in Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1505–1512, ACM, Amsterdam, Netherlands, July 2013.
  72. K. Zhao and L. Pan, “A machine learning based trust evaluation framework for online social networks,” in Proceedings of the 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, Beijing, China, September 2014.
  73. E. Dufourq and B. A. Bassett, “Automated problem identification: regression vs. classification via evolutionary deep networks,” in Proceedings of the South African Institute of Computer Scientists and Information Technologists, p. 12, Thaba Nchu, South Africa, September 2017.
  74. B. F. De Souza, A. C. P. L. F. De Carvalho, and C. Soares, “Empirical evaluation of ranking prediction methods for gene expression data classification,” in Lecture Notes in Computer Science, Springer, Berlin, Germany, 2010. View at Google Scholar
  75. M. Unger, B. Shapira, L. Rokach, and A. Bar, “Inferring contextual preferences using deep auto-encoding,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 221–229, ACM, Bratislava, Slovakia, July 2017.
  76. W. Yunli, “Automatic recognition of text difficulty from consumers health information,” in Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Salt Lake City, Utah, 2006.
  77. R. Vainshtein, A. Greenstein-Messica, G. Katz, B. Shapira, and L. Rokach, “A hybrid approach for automatic model recommendation,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1623–1626, ACM, Turin, Italy, October 2018.
  78. L. Jiang and H. Zhang, “Learning instance greedily cloning naive Bayes for ranking,” in Proceedings of the IEEE International Conference on Data Mining (ICDM), p. 8, IEEE, Houston, TX, USA, 2005.
  79. Z. Qiao, S. Zhao, C. Xiao, X. Li, Y. Qin, and F. Wang, “Pairwise-ranking based collaborative recurrent neural networks for clinical event prediction,” in Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Stockholm, Sweden, July 2018.
  80. R. Ali, S. Lee, and T. C. Chung, “Accurate multi-criteria decision making methodology for recommending machine learning algorithm,” Expert Systems with Applications, vol. 71, pp. 257–278, 2017. View at Publisher · View at Google Scholar · View at Scopus
  81. R. Lafta, J. Zhang, X. Tao et al., “A general extensible learning approach for multi-disease recommendations in a telehealth environment,” Pattern Recognition Letters, 2018. View at Publisher · View at Google Scholar · View at Scopus
  82. S. Bag, S. K. Kumar, and M. K. Tiwari, “An efficient recommendation generation using relevant jaccard similarity,” Information Sciences, vol. 483, pp. 53–64, 2019. View at Publisher · View at Google Scholar · View at Scopus
  83. A. Soudani and W. Barhoumi, “An image-based segmentation recommender using crowdsourcing and transfer learning for skin lesion extraction,” Expert Systems with Applications, vol. 118, pp. 400–410, 2019. View at Publisher · View at Google Scholar · View at Scopus
  84. S. S. Durduran, “Automatic classification of high resolution land cover using a new data weighting procedure: the combination of k-means clustering algorithm and central tendency measures (KMC-CTM),” Applied Soft Computing, vol. 35, pp. 136–150, 2015. View at Publisher · View at Google Scholar · View at Scopus
  85. C. L. Chi, W. N. Street, and M. M. Ward, “Building a hospital referral expert system with a prediction and optimization-based decision support system algorithm,” Journal of Biomedical Informatics, vol. 41, no. 2, pp. 371–386, 2008. View at Publisher · View at Google Scholar · View at Scopus
  86. N. Pombo, N. Garcia, and K. Bousson, “Classification techniques on computerized systems to predict and/or to detect apnea: a systematic review,” Computer Methods and Programs in Biomedicine, vol. 140, pp. 265–274, 2017. View at Publisher · View at Google Scholar · View at Scopus
  87. J. Szymański and J. Rzeniewicz, “Identification of category associations using a multilabel classifier,” Expert Systems with Applications, vol. 61, pp. 327–342, 2016. View at Publisher · View at Google Scholar · View at Scopus
  88. J. Pinho Lucas, S. Segrera, and M. N. Moreno, “Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems,” Expert Systems with Applications, vol. 39, no. 1, pp. 1273–1283, 2012. View at Publisher · View at Google Scholar · View at Scopus
  89. R. Espinosa, D. García-Saiz, M. Zorrilla, J. J. Zubcoff, and J. N. Mazón, “S3mining: a model-driven engineering approach for supporting novice data miners in selecting suitable classifiers,” Computer Standards & Interfaces, vol. 65, pp. 143–158, 2019. View at Publisher · View at Google Scholar · View at Scopus
  90. D. Cournapeau, “Scikit-learn,” 2019. View at Google Scholar
  91. N. Hug, “Surprise,” 2019. View at Google Scholar
  92. M. Kula, “LightFM,” in Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems Co-Located with 9th ACM, Vienna, Austria, September 2015.
  93. K. Vand, “Rexy,” 2019. View at Google Scholar
  94. A. S. Foundation, PredictionIO, A. S. Foundation, Pune, Maharashtra, 2019.
  95. G. Jenson, “HapiGER,” 2019. View at Google Scholar
  96. L. C. A. and Credits, “LensKit,” 2019. View at Google Scholar
  97. I. SuggestGrid, “SuggestGrid,” 2019. View at Google Scholar
  98. S. Systems, “SLI Systems Recommender,” 2019. View at Google Scholar
  99. A. W. Services, “AmazonWebService Machine Learning,” 2019. View at Google Scholar
  100. Microsoft, “Azure ML Studio,” 2019. View at Google Scholar
  101. Gravity Research & Development, “Yusp,” 2019. View at Google Scholar
  102. IBM Watson Studio, “IBM Watson,” 2019. View at Google Scholar
  103. Recombee, “Recombee,” 2019. View at Google Scholar
  104. Mr. Dlib, “Mr. DLib,” 2019. View at Google Scholar
  105. Caret, “Caret,” 2019. View at Google Scholar
  106. Shiny, “Shiny,” 2019. View at Google Scholar
  107. RandomForest, “RandomForest,” 2019. View at Google Scholar
  108. KlaR, “KlaR,” 2019. View at Google Scholar
  109. CORElearn, “CORElearn,” 2019. View at Google Scholar
  110. RecommenderLab, “RecommenderLab,” 2019. View at Google Scholar
  111. F. J. Domínguez-Mayo, M. J. Escalona, and M. Mejías, “QuEF (quality evaluation framework) for model-driven web methodologies,” in Lecture Notes in Computer Science, Springer, Berlin, Germany, 2010. View at Google Scholar